"""Тесты метрик и статистических критериев."""

from __future__ import annotations

import numpy as np
import pandas as pd

from stylometry.evaluation import (
    bootstrap_ci,
    classification_metrics,
    confusion_matrix_frame,
    per_class_report,
)
from stylometry.stats import (
    bootstrap_difference,
    holm_bonferroni,
    mcnemar_exact,
    wilcoxon_signed_rank,
)


def test_classification_metrics_perfect() -> None:
    y_true = np.array(["a", "b", "c", "a", "b", "c"])
    y_pred = y_true.copy()
    metrics = classification_metrics(y_true, y_pred)
    assert metrics["accuracy"] == 1.0
    assert metrics["macro_f1"] == 1.0
    assert metrics["balanced_accuracy"] == 1.0


def test_confusion_matrix_and_report() -> None:
    y_true = np.array(["a", "a", "b", "b"])
    y_pred = np.array(["a", "b", "b", "b"])
    matrix = confusion_matrix_frame(y_true, y_pred, ["a", "b"])
    assert matrix.loc["a", "a"] == 1
    report = per_class_report(y_true, y_pred, ["a", "b"])
    assert {"precision", "recall", "f1", "support"} <= set(report.columns)


def test_bootstrap_ci() -> None:
    y_true = np.array(["a", "b", "a", "b", "a", "b"])
    y_pred = np.array(["a", "b", "a", "a", "a", "b"])
    interval = bootstrap_ci(y_true, y_pred, ["a", "b"], n_boot=50, seed=1)
    assert interval["lower"] <= interval["upper"]


def test_mcnemar() -> None:
    y_true = np.array([1, 1, 0, 0, 1, 0])
    pred_a = np.array([1, 1, 0, 0, 1, 0])
    pred_b = np.array([1, 0, 0, 1, 1, 0])
    result = mcnemar_exact(y_true, pred_a, pred_b)
    assert result["b"] >= 0 and result["c"] >= 0
    assert 0 <= result["p_value"] <= 1


def test_holm() -> None:
    adjusted = holm_bonferroni([0.01, 0.02, 0.03])
    assert len(adjusted) == 3
    assert all(0 <= value <= 1 for value in adjusted)


def test_wilcoxon() -> None:
    result = wilcoxon_signed_rank([0.5, 0.6, 0.7], [0.4, 0.6, 0.5])
    assert 0 <= result["p_value"] <= 1


def test_bootstrap_difference() -> None:
    y_true = np.array([0, 1, 2, 0, 1, 2])
    pred_a = np.array([0, 1, 2, 0, 1, 2])
    pred_b = np.array([0, 1, 1, 0, 1, 1])
    result = bootstrap_difference(y_true, pred_a, pred_b, labels=[0, 1, 2], n_boot=50)
    assert result["lower"] <= result["upper"]